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Neuro-Fuzzy based Multimodal Medical Image Fusion

机译:基于神经模糊的多模态医学图像融合

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The role of fusion of images in determining the quality of medical images for diagnosis and treatment planning is important. Present work deals with fusion in wavelet domain with approximation components applied with Principal Component Analysis(PCA) fusion rule and detailed components with Neuro-Fuzzy rule. The pca helps to keep structural information in each position. Neuro-Fuzzy computing allows better intelligent decision-making in the selection of detailed components. The proposed algorithm provides better visualization and tissue structure of the organ. The proposed algorithm can work for both color as well as grayscale images. The proposed fusion algorithm tested with four pairs of data sets which are collected from defense journal. Well-known quantitative measures and subjective assessment by a team of expert radiologists validates the study of the proposed algorithm.
机译:图像融合在确定用于诊断和治疗计划的医学图像质量中的作用很重要。目前的工作涉及小波域中的融合,其中近似分量应用主分量分析(PCA)融合规则,而详细分量应用Neuro-Fuzzy规则。 pca有助于在每个位置保留结构信息。 Neuro-Fuzzy计算可以在选择详细组件时更好地进行智能决策。所提出的算法提供了更好的器官可视化和组织结构。所提出的算法既可以用于彩色图像也可以用于灰度图像。所提出的融合算法使用从国防杂志收集的四对数据集进行了测试。放射线专家团队的著名定量测量和主观评估验证了所提出算法的研究结果。

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